# 数据透视表

我们目前所用的累计操作都是按照一个维度进行，数据透视表可以看做是按照二维进行累计的操作功能。


```python
# 以泰坦尼克号数据为例子进行展示

import numpy as np
import pandas as pd
import seaborn as sns

titanic = sns.load_dataset("titanic")
print(titanic.shape)
```

    (891, 15)


## 数据透视表的初步使用


```python
# 先进行粗分类
a = titanic.groupby("sex")[['survived']].mean()

# 可以看出，女性获救比例大概是男性的4倍多
print(a)
```

            survived
    sex             
    female  0.742038
    male    0.188908



```python
# 尝试按sex，class分组，然后统计逃生人数，求mean后使用层级索引
# 可以清晰的展示出，逃生人数受sex，class 的影响

a = titanic.groupby(['sex', 'class'])['survived'].aggregate('mean').unstack()
print(a)
```

    class      First    Second     Third
    sex                                 
    female  0.968085  0.921053  0.500000
    male    0.368852  0.157407  0.135447


## pivot_table

pivot_table实现的效果等同于上一届的管道命令，是一个简写。


```python
# 尝试按sex，class分组，然后统计逃生人数，求mean后使用层级索引
a = titanic.pivot_table('survived', index='sex', columns='class')
print(a)
```

    class      First    Second     Third
    sex                                 
    female  0.968085  0.921053  0.500000
    male    0.368852  0.157407  0.135447


## 创建多级索引形状的DataFrame结果



```python
# 按照sex，age，class统计，age分三个年龄段,0-18-80

# 使用cut函数对年龄进行分段
# cut适用于明确知道分界值的时候进行分类，具体落在每个分界内的数据数量不一定
age = pd.cut(titanic['age'], [0,18,80])

a = titanic.pivot_table('survived', ['sex', age], 'class')
print(a)
```

    class               First    Second     Third
    sex    age                                   
    female (0, 18]   0.909091  1.000000  0.511628
           (18, 80]  0.972973  0.900000  0.423729
    male   (0, 18]   0.800000  0.600000  0.215686
           (18, 80]  0.375000  0.071429  0.133663



```python
# 对列也可以使用类似策略
# 使用qcut对票价进行划分成两部分，每一部分人数相等
# qcut把数据分成两部分，一般是等分
fare = pd.qcut(titanic['fare'], 2)

a = titanic.pivot_table('survived', ['sex', age], [fare, 'class'])
print(a)
```

    fare            (-0.001, 14.454]                     (14.454, 512.329]  \
    class                      First    Second     Third             First   
    sex    age                                                               
    female (0, 18]               NaN  1.000000  0.714286          0.909091   
           (18, 80]              NaN  0.880000  0.444444          0.972973   
    male   (0, 18]               NaN  0.000000  0.260870          0.800000   
           (18, 80]              0.0  0.098039  0.125000          0.391304   
    
    fare                                 
    class              Second     Third  
    sex    age                           
    female (0, 18]   1.000000  0.318182  
           (18, 80]  0.914286  0.391304  
    male   (0, 18]   0.818182  0.178571  
           (18, 80]  0.030303  0.192308